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Using soft computing methods in the design of intelligent controllers

Number of pages: 79 File Format: word File Code: 32123
Year: 2013 University Degree: Master's degree Category: Electronic Engineering
Tags/Keywords: Anil's simulations - Controller design - Fuzzy sets - Genetic algorithms - Neural networks - Robot control - Smart controllers - Soft computing methods
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    Seminar to obtain a master's degree

    In the field of electrical engineering, electronics orientation

    Abstract

    Mechanization of tools is one of the most important and extensive fields used in production processes. Due to the complexity and uncertainty of machining processes, recently soft computing techniques [1] based on physical models have been preferred to predict the performance of machining processes and optimize them over conventional methods. The main soft computing tools used for this purpose are neural networks[2], fuzzy set theory, genetic algorithms, annealing simulations[3], ant colony optimization and particle swarm optimization. In advanced systems that try to control a part or a device, using a robot is an important matter. The degree of control over the behavior and movements of this robot, depending on the purpose of its use, may include a wide range. For example, not much accuracy is needed in industrial applications, but very high accuracy is expected in the case of robots used in medical fields. This series examines the use of soft computing methods in intelligent control processes and the results of using these methods in designing the control system of a robot with flexible behavior, which results indicate a significant improvement in the mechanization and design of control systems with the mentioned methods.

    Intelligent controllers, robot control

    1-1- Introduction

    The main idea of ??soft computing was introduced in 1981 by Professor Zadeh [1] in the first article published by him called "What is soft computing". In his definition, soft computing was a combination of several areas, including fuzzy logic, neural computing[2], evolutionary processes, genetic computing, and statistical computing.

    This domain leads to the combination of methods that are used to model the behavior of complex systems in the real world (practical and non-theoretical applied systems), which are often impossible to model using the computational laws of absolute mathematics and hard logic[3]. Or it is very difficult. However, by using soft computing methods, practical and practical implementations and simulations can be provided for them. This advantage makes it show a behavior similar to that of a human (which has a very high generalization ability). Then related areas such as fuzzy computing, neural computing, genetic algorithm and so on. explained and the relationship between them is explained.

    1-2- Definition of soft computing (SC)

    The definition of soft computing by Professor Zadeh in 1992 is stated as follows: "Soft computing is an emerging method for performing calculations in parallel with the remarkable ability of the human mind, reasoning and learning in an environment full of ambiguity and inaccuracy."

    Soft computing includes several examples of computing fields, including the following:

    Fuzzy systems: systems based on knowledge and awareness by if-then statements

    Neural networks: systems based on learning and adaptation

    Genetic algorithm: systems based on evolutionary calculations

    Particle swarm algorithm: systems based on minimization calculations

    The said systems are the core of a soft calculation. These systems are sometimes used alone and sometimes combined and shared to model the systems of the surrounding world. The progress of soft computing methods is not limited to these systems and is still expanding. 1-3- Objectives of soft computing. Soft computing method is better than most solving and modeling methods.

    1-3- Objectives of soft computing

    The method of soft computing is considered a newer field compared to most multidisciplinary modeling and solution methods[4] due to its great diversity. In the structures that need to build a smart system based on artificial intelligence - which need smart calculations - soft computing methods can be used with high confidence. Problems that often cannot be modeled with mathematical rules.

    In problems that are mixed with approximation[5], uncertainty[6], inaccuracy[7] and relative accuracy[8], the use of soft computing methods is the best choice to reach decisions similar to the decisions of a human.

    In order to further clarify the explanation issue It is presented about the mentioned systems:

    Approximation: Here, the features of the model are very similar to the real sample, but not exactly the same.

    Uncertainty: The belief that there is about the features is that there is no 100% certainty of their correctness.

    Inaccuracy: the features of the model are the same as the features of the sample. They are not real, but they are very close to them.

    1-4- Importance of soft computing

    As it is clear from its name and also the explanations that have been discussed so far, these calculations are different from hard calculations[9]. Unlike hard computing methods, soft computing has good flexibility against inaccuracy, uncertainty, approximation and relative accuracy. The importance of using soft computing methods is determined when, with less cost and time, higher accuracy and more flexibility, nonlinear and complex physical systems can be modeled in a way that matches the decisions of an expert with a high percentage of accuracy.

    The important point is that soft computing is not exactly a combination[10], mixture[11] or integration[12], while soft computing is a partnership. It is considered that each member moves towards the intended goal in his own unique way. Basically, the main component in soft computing is complementarity, not competition. Therefore, soft computing is considered an emerging foundation in cognitive intelligence.

  • Contents & References of Using soft computing methods in the design of intelligent controllers

    List:

    Presentation A

    Thanks and appreciation B

    Abstract T

    List of contents D

    List of figures H

    List of tables H

    1: Introduction of basics and main concepts 1

    1-1- Introduction. 2

    1-2- Definition of soft computing (SC) 3

    1-3- Objectives of soft computing. 4

    1-4- The importance of soft computing. 5

    2: Fuzzy computing, neural computing and algorithms based on genetics and particle swarm algorithm 6

    2-1- Fuzzy logic. 7

    2-1-1- The difference between fuzzy sets and classical sets. 8

    2-1-2- Dry and non-dry sets. 9

    2-1-3- description of fuzzy sets. 10

    2-1-4- The process of using fuzzy logic. 11

    2-1-5- Fuzzy logic and its connection with artificial intelligence. 13

    2-2- neural networks. 14

    2-2-1- An introduction to artificial neural networks. 14

    2-2-2- similarity with the brain. 14

    2-2-3- Artificial neural networks. 17

    2-2-4- artificial nerve cell. 18

    2-2-5- The structure of artificial neural networks and their function. 19

    2-2-6- Division of neural networks based on structure. 21

    2-2-7- Division of neural networks based on learning algorithm. 22

    2-2-8- A general view on network education. 23

    2-3- evolutionary optimization algorithms. 25

    2-4- Genetic algorithm. 26

    2-4-1- Introduction. 26

    2-4-2- Chromosome display. 29

    2-4-3- Encoding maps. 31

    2-4-4- Population initialization. 32

    2-4-5- Proportion function. 33

    2-4-6- genetic operators. 34

    2-4-7- selection methods. 38

    2-5- Particle Swarm Algorithm (PSO) 40

    3: Application of fuzzy logic in mobile robots 44

    3-1- History. 45

    3-2- Introduction. 45

    3-3- Reasons for using fuzzy controllers. 46

    3-4- The structure of a fuzzy controller. 47

    3-5- Fuzzy methods used in robots. 49

    3-5-1- position control in moving robots. 50

    4: controller design based on soft computing 56

    4-1- Soft computing techniques. 57

    4-2- Feedback control proportional to derivative and acceleration. 60

    4-3- Multivariable fuzzy logic controllers. 62

    4-4- Fuzzy neural control systems (HFNC) 63

    4-4-1-Radial basis function neural model training (RBFNN). 65

    References and source: 69

     

    Source:

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    [[1]]   Teodorescu, H. N. L., Kandell, A., & Jain, L. C. (Eds.). (1999). Soft-computing in Human-related Sciences (Vol. 8). CRC press.

     

    [[1]] Mitchell, M. (1998). An introduction to genetic algorithms (complex adaptive systems). swarm optimization, in: IEEE Conf. on Neural Networks, 1942-1948, M. Jamshidi,Jamshidi, Autonomous control of complex systems: robotic applications, Applied Mathematics and Computation, vol. 120, Issues 1–3, 10 May 2001, pp. 15-29.

    [[1]] G. Feng, A survey on analysis and design of model-based fuzzy control systems, IEEE Trans. Syst. Man Cybernetics 14 (5) (2006) 676–696.

     

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